If you are new to R, then perhaps a look at simple univariate data is a good place to start. In thisRPubs post, I take a look at both categorical and numerical data. It is quite easy to calculate descriptive statistics of univariate data and to visualize it using plots. Click the link and have a look.

The World Bank provides open data for many indicators across most countries, spanning the last few decades.

This data is available online with searches available by country codes (iso2c and iso3c), indicator names, and by dates. The indicators can be viewed here. It can also be accessed via an application programming interface (API). The WDI library in R provides access through this API, allowing for easy search and retrieval of data.

In this post, written as an R-markdown file, and available on RPubs andGitHub, I showcase the WDI library by looking at maternal mortality rates for the United States, Brazil, and South Africa.

In this post, written as an R-markdown file and posted on RPubs, I discuss the assumptions for the use of parametric tests in R.

Parametric tests such as the various t tests, analysis of variance (ANOVA), and correlations are only valid if certain assumptions are met. When these assumptions are not met, the use of these tests in your research may lead to false claims.

In the post I show you the most important assumptions and how to test for them using the R programming language.

R is a programming language designed by statisticians for statistical analysis. It is a free programming language and is available for download (Windows, Mac, and Linux).

Bar a few eccentricities, it is quite easy to learn R. We make extensive use of it in the Klopper Research Group, where, alongside other programming languages, I use it to teach my students how to conduct proper data analysis.

I have started to create a series of R markdown files that are published on the Rpubs website . I am also making a series of YouTube videos on the use of R. The first set is on the use of the Plotly library to create interactive HTML widget plots in R.

In this post I discuss some of the assumptions that must be met for the use of parametric statistical tests. The post contain snippets in the R statistical programming language to help visualize the concepts and to show how these assumptions are tested. Click on the link above to view the post.